Computational Aesthetics and Philosophical Interpretation of Decorative Pattern Evolution in Ancient Chinese Ceramics: A Multi-Scale Fractal and Semantic Analysis Approach
DOI:
https://doi.org/10.64504/big.d.v3i3.777Keywords:
Computational aesthetics; fractal analysis; Chinese ceramics; cultural heritage; decorative evolution; image processing; visual complexityAbstract
Decorative patterns on ancient Chinese ceramics provide valuable visual evidence for examining historical changes in artistic form, technological practice, and cultural aesthetics. Previous studies of Chinese ceramic decoration have primarily relied on qualitative stylistic description, which limits reproducible comparison across long chronological periods. This study presents an exploratory computational framework for analyzing the formal complexity of ceramic decorative patterns using image-based descriptors and fractal analysis. A dataset of 1,200 ceramic pattern images representing twelve historical periods, from the Yangshao culture to the Qing dynasty, was examined. After grayscale conversion and Otsu thresholding, three visual features were extracted: fractal dimension, aspect ratio, and black-white ratio. These features were used to evaluate temporal changes in pattern complexity and to explore potential stylistic groupings through K-means clustering. The results suggest a general increase in visual complexity from early Neolithic ceramics to later dynastic periods, with higher complexity observed in Tang, Song, Yuan, Ming, and Qing decorative patterns. The clustering results further indicate that image-based complexity measures can distinguish broad stylistic tendencies across historical periods. Rather than claiming a definitive model of aesthetic evolution, this study provides a reproducible computational approach for supporting art-historical interpretation of ceramic decoration. The findings demonstrate the potential of combining computer vision, fractal geometry, and cultural heritage analysis, while also highlighting the need for better-documented datasets and further validation in future research.
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